Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

The spin-forbidden transition in iron(IV)-oxo catalysts relevant to two-state reactivity

Quintet oxoiron(IV) intermediates are often invoked in nonheme iron enzymes capable of performing selective oxidation, while most well-characterized synthetic model oxoiron(IV) complexes have a triplet ground state. These differing spin states lead to the proposal of a two-state reactivity model, where the complexes cross from the triplet to an excited quintet state. However, the energy of this quintet state has never been measured experimentally. Here, magnetic circular dichroism is used to assign the singlet and triplet excited states in a series of triplet oxoiron(IV) complexes. These transition energies are used to determine the energies of the quintet state via constrained fitting of 2p3d resonant inelastic x-ray scattering. This allowed for a direct correlation between the quintet energies and substrate C─H oxidation rates.

Low-Temperature Oxidation of Methane on Rutile TiO2(110): Identifying the Role of Surface Oxygen Species

Understanding the microkinetic mechanism underlying photocatalytic oxidative methane (CH4) conversion is of significant importance for the successful design of efficient catalysts. Herein, CH4 photooxidation has been systematically investigated on oxidized rutile(R)-TiO2(110) at 60 K. Under 355 nm irradiation, the C-H bond activation of CH4 is accomplished by the hole-trapped dangling OTi- center rather than the hole-trapped Ob- center via the Eley-Rideal reaction pathway, producing movable CH3• radicals. Subsequently, movable CH3• radicals encounter an O/OH species to form CH3O/CH3OH species, which could further dissociate into CH2O under irradiation. However, the majority of the CH3• radical intermediate is ejected into a vacuum, which may induce radical-mediated reactions under ambient conditions. The result not only advances our knowledge about inert C-H bond activation but also provides a deep insight into the mechanism of photocatalytic CH4 conversion, which will be helpful for the successful design of efficient catalysts.

Structure and Function of Alkane Monooxygenase (AlkB)

Every year, perhaps as much as 800 million tons of hydrocarbons enters the environment; alkanes make up a large percentage of it. Most are transformed by organisms that utilize these molecules as sources of energy and carbon. Both aerobic and anaerobic alkane transformation chemistries exist, capitalizing on the presence of alkanes in both oxic and anoxic environments. Over the past 40 years, tremendous progress has been made in understanding the structure and mechanism of enzymes that catalyze the transformation of methane. By contrast, progress involving enzymes that transform liquid alkanes has been slower with the first structures of AlkB, the predominant aerobic alkane hydroxylase in the environment, appearing in 2023. Because of the fundamental importance of C-H bond activation chemistries, interest in understanding how biology activates and transforms alkanes is high.In this Account, we focus on steps we have taken to understand the mechanism and structure of alkane monooxygenase (AlkB), the metalloenzyme that dominates the transformation of liquid alkanes in the environment (not to be confused with another AlkB that is an α-ketogluturate-dependent enzyme involved in DNA repair). First, we briefly describe what is known about the prevalence of AlkB in the environment and its role in the carbon cycle. Then we review the key findings from our recent high-resolution cryoEM structure of AlkB and highlight important similarities and differences in the structures of members of class III diiron enzymes. Functional studies, which we summarize, from a number of single residue variants enable us to say a great deal about how the structure of AlkB facilitates its function. Next, we overview work from our laboratories using mechanistically diagnostic radical clock substrates to characterize the mechanism of AlkB and contextualize the results we have obtained on AlkB with results we have obtained on other alkane-oxidizing enzymes and explain these results in light of the enzyme's structure. Finally, we integrate recent work in our laboratories with information from prior studies of AlkB, and relevant model systems, to create a holistic picture of the enzyme. We end by pointing to critical questions that still need to be answered, questions about the electronic structure of the active site of the enzyme throughout the reaction cycle and about whether and to what extent the enzyme plays functional roles in biology beyond simply initiating the degradation of alkanes.

HAA by the first {Mn(iii)OH} complex with all O-donor ligands

There is considerable interest in MnOHx moieties, particularly in the stepwise changes in those O-H bonds in tandem with Mn oxidation state changes. The reactivity of aquo-derived ligands, {MOHx}, is also heavily influenced by the electronic character of the other ligands. Despite the prevalence of oxygen coordination in biological systems, preparation of mononuclear Mn complexes of this type with all O-donors is rare. Herein, we report several Mn complexes with perfluoropinacolate (pinF)2- including the first example of a crystallographically characterized mononuclear {Mn(iii)OH} with all O-donors, K2[Mn(OH)(pinF)2], 3. Complex 3 is prepared via deprotonation of K[Mn(OH2)(pinF)2], 1, the pKa of which is estimated to be 18.3 ± 0.3. Cyclic voltammetry reveals quasi-reversible redox behavior for both 1 and 3 with an unusually large ΔEp, assigned to the Mn(iii/ii) couple. Using the Bordwell method, the bond dissociation free energy (BDFE) of the O-H bond in {Mn(ii)-OH2} is estimated to be 67-70 kcal mol-1. Complex 3 abstracts H-atoms from 1,2-diphenylhydrazine, 2,4,6-TTBP, and TEMPOH, the latter of which supports a PCET mechanism. Under basic conditions in air, the synthesis of 1 results in K2[Mn(OAc)(pinF)2], 2, proposed to result from the oxidation of Et2O to EtOAc by a reactive Mn species, followed by ester hydrolysis. Complex 3 alone does not react with Et2O, but addition of O2 at low temperature effects the formation of a new chromophore proposed to be a Mn(iv) species. The related complexes K(18C6)[Mn(iii)(pinF)2], 4, and (Me4N)2[Mn(ii)(pinF)2], 5, have also been prepared and their properties discussed in relation to complexes 1-3.

Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis

The challenge of direct partial oxidation of methane to methanol has motivated the targeted search of metal-organic frameworks (MOFs) as a promising class of materials for this transformation because of their site-isolated metals with tunable ligand environments. Thousands of MOFs have been synthesized, yet relatively few have been screened for their promise in methane conversion. We developed a high-throughput virtual screening workflow that identifies MOFs from a diverse space of experimental MOFs that have not been studied for catalysis, yet are thermally stable, synthesizable, and have promising unsaturated metal sites for C-H activation via a terminal metal-oxo species. We carried out density functional theory calculations of the radical rebound mechanism for methane-to-methanol conversion on models of the secondary building units (SBUs) from 87 selected MOFs. While we showed that oxo formation favorability decreases with increasing 3d filling, consistent with prior work, previously observed scaling relations between oxo formation and hydrogen atom transfer (HAT) are disrupted by the greater diversity in our MOF set. Accordingly, we focused on Mn MOFs, which favor oxo intermediates without disfavoring HAT or leading to high methanol release energies─a key feature for methane hydroxylation activity. We identified three Mn MOFs comprising unsaturated Mn centers bound to weak-field carboxylate ligands in planar or bent geometries with promising methane-to-methanol kinetics and thermodynamics. The energetic spans of these MOFs are indicative of promising turnover frequencies for methane to methanol that warrant further experimental catalytic studies.

Quantifiable polarity match effect on C-H bond cleavage reactivity and its limits in reaction design

When oxidants favour cleaving a strong C-H bond at the expense of weaker ones, which are otherwise inherently preferred due to their favourable reaction energy, reactivity factors such as the polarity match effect are often invoked. Polarity match follows the intuition of electrophilic (nucleophilic) oxidants reacting faster with nucleophilic (electrophilic) C-H bonds. Nevertheless, this concept is purely qualitative and is best suited for a posteriori rationalization of experimental observations. Here, we propose and inspect two methods to quantify polar effects in C-H cleavage reactions, one by computation via the difference of atomic charges (Δq) of reacting atoms, and one amenable to experimental measurement through asynchronicity factors, η. By their application to three case studies, we observe that both Δq and η faithfully capture the notion of polarity match. The polarity match model, however, proves insufficient as a predictor of H-atom abstraction reactivity and we discourage its use as a standalone variable in reaction design. Besides this caveat, η and Δq (through its mapping on η) allow the implementation of polarity match into a Marcus-type model of reactivity, alleviating its shortcomings and making reaction planning feasible.

High-throughput computational workflow for ligand discovery in catalysis with the CSD

A novel semi-automated, high-throughput computational workflow for ligand/catalyst discovery based on the Cambridge Structural Database is reported.

Single Metal Atoms on Oxide Surfaces: Assessing the Chemical Bond through 17O Electron Paramagnetic Resonance

ConspectusEven in the gas phase single atoms possess catalytic properties, which can be crucially enhanced and modulated by the chemical interaction with a solid support. This effect, known as electronic metal-support interaction, encompasses charge transfer, orbital overlap, coordination structure, etc., in other words, all the crucial features of the chemical bond. These very features are the object of this Account, with specific reference to open-shell (paramagnetic) single metal atoms or ions on oxide supports. Such atomically dispersed species are part of the emerging class of heterogeneous catalysts known as single-atom catalysts (SACs). In these materials, atomic dispersion ensures maximum atom utilization and uniform active sites, whereby the nature of the chemical interaction between the metal and the oxide surface modulates the catalytic activity of the metal active site by tuning the energy of the frontier orbitals. A comprehensive set of examples includes fourth period metal atoms and ions in zeolites on insulating (e.g., MgO) or reducible (e.g., TiO₂) oxides and are among the most relevant catalysts for a wealth of key processes of industrial and environmental relevance, from the abatement of NO_x to the selective oxidation of hydrocarbons and the conversion of methane to methanol.There exist several spectroscopic techniques able to inform on the geometric and electronic structure of isolated single metal ion sites, but either they yield information averaged over the bulk or they lack description of the intimate features of chemical bonding, which include covalency, ionicity, electron and spin delocalization. All of these can be recovered at once by measuring the magnetic interactions between open-shell metals and the surrounding nuclei with Electron Paramagnetic Resonance (EPR) spectroscopy. In the case of oxides, this entails the synthesis of ¹⁷O isotopically enriched materials. We have established ¹⁷O EPR as a unique source of information about the local binding environment around oxygen of magnetic atoms or ions on different oxidic supports to rationalize structure-property relationships. Here, we will describe strategies for ¹⁷O surface enrichments and approaches to monitor the state of charge and spin delocalization of atoms or ions from K to Zn dispersed on oxide surfaces characterized by different chemical properties (i.e., basicity or reducibility). Emphasis is placed on chemical insight at the atomic-scale level achieved by ¹⁷O EPR, which is a crucial step in understanding the structure-property relationships of single metal atom catalysts and in enabling efficient design of future materials for a range of end uses.

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimizations on the fly, and exploit its good performance and transferability in identifying geometry optimization failures for catalyst design. We show that the dynamic classifier performs well on all reactive intermediates in the representative catalytic cycle of the radical rebound mechanism for the conversion of methane to methanol despite being trained on only one reactive intermediate. The dynamic classifier also generalizes to chemically distinct intermediates and metal centers absent from the training data without loss of accuracy or model confidence. We rationalize this superior model transferability as arising from the use of electronic structure and geometric information generated on-the-fly from density functional theory calculations and the convolutional layer in the dynamic classifier. When used in combination with uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning-accelerated density functional theory in a space of 16 M candidate catalysts including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low-spin Fe(II) compounds paired with strong-field (e.g., P or S-coordinating) ligands have among the best energetic tradeoffs between hydrogen atom transfer (HAT) and methanol release. This observation contrasts with prior efforts that have focused on high-spin Fe(II) with weak-field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and that higher-valency metals [i.e., M(III) vs M(II)] are likely to be rate-limited by slow methanol release. With full characterization of barrier heights, we confirm that optimizing for HAT does not lead to large oxo formation barriers. Energetic span analysis reveals designs for an intermediate-spin Mn(II) catalyst and a low-spin Fe(II) catalyst that are predicted to have good turnover frequencies. Our active learning approach to optimize two distinct reaction energies with efficient global optimization is expected to be beneficial for the search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown.

Bonding and stability of dinitrogen-bonded donor base-stabilized Si(0)/Ge(0) species [(cAACMe-Si/Ge)2(N2)]: EDA-NOCV analysis

Recently, dinitrogen (N₂) binding and its activation have been achieved by non-metal compounds like intermediate cAAC-borylene as (cAAC)₂(B-Dur)₂(N₂) [cAAC = cyclic alkyl(amino) carbene; Dur = aryl group, 2,3,5,6-tetramethylphenyl; B-Dur = borylene]. It has attracted a lot of scientific attention from different research areas because of its future prospects as a potent species towards the metal free reduction of N₂ into ammonia (NH₃) under mild conditions. Two (cAAC)(B-Dur) units, each of which possesses six valence electrons around the B-centre, are shown to accept σ-donations from the N₂ ligand (B ← N₂). Two B-Dur further provide π-backdonations (B → N₂) to a central N₂ ligand to strengthen the B–N₂–B bond, providing maximum stability to the compound (cAAC)₂(B-Dur)₂(N₂) since the summation of each pair wise interaction accounted for the total stabilization energy of the molecule. (cAAC)(B-Dur) unit is isolobal to cAAC–E (E = Si, Ge) fragment. Herein, we report on the stability and bonding of cAAC–E bonded N₂-complex (cAAC–E)₂(N₂) (1–2; Si, Ge) by NBO, QTAIM and EDA-NOCV analyses (EDA-NOCV = energy decomposition analysis coupled with natural orbital for chemical valence; QTAIM = quantum theory of atoms in molecule). Our calculation suggested that syntheses of elusive (cAAC–E)₂(N₂) (1–2; Si, Ge) species may be possible with cAAC ligands having bulky substitutions adjacent to the C_cAAC atom by preventing the homo-dimerization of two (cAAC)(E) units which can lead to the formation of (cAAC–E)₂. The formation of E Created by potrace 1.16, written by Peter Selinger 2001-2019 ]]> E bond is thermodynamically more favourable (E = Si, Ge) over binding energy of N₂ inbetween two cAAC–E units.

Dinitrogen (N₂) binding and its activation have been achieved by non-metal compounds like intermediate cAACborylene with the general formula of (cAAC)₂(B-Dur)₂(N₂) [cAAC = cyclic alkyl(amino)carbene; Dur = aryl group, 2,3,5,6-tetramethylphenyl; B-Dur = aryl-borylene].

Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation

While computational screening with first-principles density functional theory (DFT) is essential for evaluating candidate catalysts, limitations in accuracy typically prevent the prediction of experimentally relevant activities. Exemplary of these challenges are homogeneous water oxidation catalysts (WOCs) where differences in experimental conditions or small changes in ligand structure can alter rate constants by over an order of magnitude. Here, we compute mechanistically relevant electronic and energetic properties for 19 mononuclear Ru transition-metal complexes (TMCs) from three experimental water oxidation catalysis studies. We discover that 15 of these TMCs have experimental activities that correlate with a single property, the ionization potential of the Ru(II)-O₂ catalytic intermediate. This scaling parameter allows the quantitative understanding of activity trends and provides insight into the rate-limiting behavior. We use this approach to rationalize differences in activity with different experimental conditions, and we qualitatively analyze the source of distinct behavior for different electronic states in the other four catalysts. Comparison to closely related single-atom catalysts and modified WOCs enables rationalization of the source of rate enhancement in these WOCs.

Recent progress in rare-earth metal-catalyzed sp2 and sp3 C–H functionalization to construct C–C and C–heteroelement bonds

The review presents rare-earth metal-catalyzed C(sp2/sp3)–H functionalization accessing C–C/C–heteroatom bonds and olefin (co)polymerization, highlighting substrate scope, mechanistic realization, and origin of site-, enantio-/diastereo-selectivity.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

A framework for constructing linear free energy relationships to design molecular transition metal catalysts

A computational framework for ligand-driven design of transition metal complexes is presented in this work. We propose a general procedure for the construction of active site-specific linear free energy relationships (LFERs), which are inspired from Hammett and Taft correlations in organic chemistry and grounded in the activation strain model (ASM). Ligand effects are isolated and quantified in terms of their contribution to interaction and strain energy components of ASM. Scalar descriptors that are easily obtainable are then employed to construct the complete LFER. We successfully demonstrate proof-of-concept by constructing and applying an LFER to CH activation with enzyme-inspired [Cu2O2]2+ complexes. The key benefit of using ASM is a built-in compensation or error cancellation between LFER prediction of interaction and strain terms, resulting in accurate barrier predictions for 37 of the 47 catalysts examined in this study. The LFER is also transferable with respect to level of theory and flexible towards the choice of reference system. The absence of interaction-strain compensation or poor model performance for the remaining systems is a consequence of the approximate nature of the chosen interaction energy descriptor and LFER construction of the strain term, which focuses largely on trends in substrate and not catalyst strain.

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doomed to fail. Many compelling functional materials and catalytic processes involve strained chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to open-shell transition-metal centers. Although promising targets, these materials present unique challenges for electronic structure methods and combinatorial challenges for their discovery. In this Perspective, we describe the advances needed in accuracy, efficiency, and approach beyond what is typical in conventional DFT-based ML workflows. These challenges have begun to be addressed through ML models trained to predict the results of multiple methods or the differences between them, enabling quantitative sensitivity analysis. For DFT to be trusted for a given data point in a high-throughput screen, it must pass a series of tests. ML models that predict the likelihood of calculation success and detect the presence of strong correlation will enable rapid diagnoses and adaptation strategies. These "decision engines" represent the first steps toward autonomous workflows that avoid the need for expert determination of the robustness of DFT-based materials discoveries.

Statistical analysis of C-H activation by oxo complexes supports diverse thermodynamic control over reactivity

Transition metal oxo species are key intermediates for the activation of strong C-H bonds. As such, there has been interest in understanding which structural or electronic parameters of metal oxo complexes determine their reactivity. Factors such as ground state thermodynamics, spin state, steric environment, oxygen radical character, and asynchronicity have all been cited as key contributors, yet there is no consensus on when each of these parameters is significant or the relative magnitude of their effects. Herein, we present a thorough statistical analysis of parameters that have been proposed to influence transition metal oxo mediated C-H activation. We used density functional theory (DFT) to compute parameters for transition metal oxo complexes and analyzed their ability to explain and predict an extensive data set of experimentally determined reaction barriers. We found that, in general, only thermodynamic parameters play a statistically significant role. Notably, however, there are independent and significant contributions from the oxidation potential and basicity of the oxo complexes which suggest a more complicated thermodynamic picture than what has been shown previously.